{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cql/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from bert4keras.backend import keras, set_gelu\n",
    "from bert4keras.tokenizers import Tokenizer\n",
    "from bert4keras.models import build_transformer_model\n",
    "from bert4keras.optimizers import Adam, extend_with_piecewise_linear_lr\n",
    "from bert4keras.snippets import sequence_padding, DataGenerator\n",
    "from bert4keras.snippets import open\n",
    "from keras.layers import Lambda, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "set_gelu('tanh')  # 切换gelu版本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(filename):\n",
    "    df = pd.read_csv(filename, header=0, encoding='utf8')\n",
    "    f = df[['char','class']].values\n",
    "    D = []\n",
    "    \n",
    "    for i,l in enumerate(f):\n",
    "        text, label = l\n",
    "        D.append((text, int(label)))\n",
    "    return D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = load_data('data/train.csv')\n",
    "valid_data = load_data('data/val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((164151, 2), (29776, 2))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(train_data).shape, np.array(valid_data).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 微调Bert_rbt3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bert rbtl3\n",
    "num_classes = 26\n",
    "maxlen = 180\n",
    "batch_size = 128\n",
    "model_type = 'bert'\n",
    "best_model = './model/bert/bert_rbt3_2.weights'\n",
    "\n",
    "config_path = '/data/models/embedding/chinese_rbt3_L-3_H-768_A-12/bert_config_rbt3.json'\n",
    "checkpoint_path = '/data/models/embedding/chinese_rbt3_L-3_H-768_A-12/bert_model.ckpt'\n",
    "dict_path = '/data/models/embedding/chinese_rbt3_L-3_H-768_A-12/vocab.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/cql/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Input-Token (InputLayer)        (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Input-Segment (InputLayer)      (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Embedding-Token (Embedding)     (None, None, 768)    16226304    Input-Token[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "Embedding-Segment (Embedding)   (None, None, 768)    1536        Input-Segment[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "Embedding-Token-Segment (Add)   (None, None, 768)    0           Embedding-Token[0][0]            \n",
      "                                                                 Embedding-Segment[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Embedding-Position (PositionEmb (None, None, 768)    393216      Embedding-Token-Segment[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "Embedding-Norm (LayerNormalizat (None, None, 768)    1536        Embedding-Position[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "Embedding-Dropout (Dropout)     (None, None, 768)    0           Embedding-Norm[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-MultiHeadSelfAtte (None, None, 768)    2362368     Embedding-Dropout[0][0]          \n",
      "                                                                 Embedding-Dropout[0][0]          \n",
      "                                                                 Embedding-Dropout[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-MultiHeadSelfAtte (None, None, 768)    0           Transformer-0-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-MultiHeadSelfAtte (None, None, 768)    0           Embedding-Dropout[0][0]          \n",
      "                                                                 Transformer-0-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-MultiHeadSelfAtte (None, None, 768)    1536        Transformer-0-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-FeedForward (Feed (None, None, 768)    4722432     Transformer-0-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-FeedForward-Dropo (None, None, 768)    0           Transformer-0-FeedForward[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-FeedForward-Add ( (None, None, 768)    0           Transformer-0-MultiHeadSelfAttent\n",
      "                                                                 Transformer-0-FeedForward-Dropout\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-0-FeedForward-Norm  (None, None, 768)    1536        Transformer-0-FeedForward-Add[0][\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-MultiHeadSelfAtte (None, None, 768)    2362368     Transformer-0-FeedForward-Norm[0]\n",
      "                                                                 Transformer-0-FeedForward-Norm[0]\n",
      "                                                                 Transformer-0-FeedForward-Norm[0]\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-MultiHeadSelfAtte (None, None, 768)    0           Transformer-1-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-MultiHeadSelfAtte (None, None, 768)    0           Transformer-0-FeedForward-Norm[0]\n",
      "                                                                 Transformer-1-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-MultiHeadSelfAtte (None, None, 768)    1536        Transformer-1-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-FeedForward (Feed (None, None, 768)    4722432     Transformer-1-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-FeedForward-Dropo (None, None, 768)    0           Transformer-1-FeedForward[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-FeedForward-Add ( (None, None, 768)    0           Transformer-1-MultiHeadSelfAttent\n",
      "                                                                 Transformer-1-FeedForward-Dropout\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-1-FeedForward-Norm  (None, None, 768)    1536        Transformer-1-FeedForward-Add[0][\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-MultiHeadSelfAtte (None, None, 768)    2362368     Transformer-1-FeedForward-Norm[0]\n",
      "                                                                 Transformer-1-FeedForward-Norm[0]\n",
      "                                                                 Transformer-1-FeedForward-Norm[0]\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-MultiHeadSelfAtte (None, None, 768)    0           Transformer-2-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-MultiHeadSelfAtte (None, None, 768)    0           Transformer-1-FeedForward-Norm[0]\n",
      "                                                                 Transformer-2-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-MultiHeadSelfAtte (None, None, 768)    1536        Transformer-2-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-FeedForward (Feed (None, None, 768)    4722432     Transformer-2-MultiHeadSelfAttent\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-FeedForward-Dropo (None, None, 768)    0           Transformer-2-FeedForward[0][0]  \n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-FeedForward-Add ( (None, None, 768)    0           Transformer-2-MultiHeadSelfAttent\n",
      "                                                                 Transformer-2-FeedForward-Dropout\n",
      "__________________________________________________________________________________________________\n",
      "Transformer-2-FeedForward-Norm  (None, None, 768)    1536        Transformer-2-FeedForward-Add[0][\n",
      "__________________________________________________________________________________________________\n",
      "CLS-token (Lambda)              (None, 768)          0           Transformer-2-FeedForward-Norm[0]\n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 26)           19994       CLS-token[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 37,906,202\n",
      "Trainable params: 37,906,202\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 建立分词器\n",
    "tokenizer = Tokenizer(dict_path, do_lower_case=True)\n",
    "\n",
    "class data_generator(DataGenerator):\n",
    "    \"\"\"数据生成器\n",
    "    \"\"\"\n",
    "    def __iter__(self, random=False):\n",
    "        batch_token_ids, batch_segment_ids, batch_labels = [], [], []\n",
    "        for is_end, (text, label) in self.sample(random):\n",
    "            token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)\n",
    "            batch_token_ids.append(token_ids)\n",
    "            batch_segment_ids.append(segment_ids)\n",
    "            batch_labels.append([label])\n",
    "            if len(batch_token_ids) == self.batch_size or is_end:\n",
    "                batch_token_ids = sequence_padding(batch_token_ids)\n",
    "                batch_segment_ids = sequence_padding(batch_segment_ids)\n",
    "                batch_labels = sequence_padding(batch_labels)\n",
    "                yield [batch_token_ids, batch_segment_ids], batch_labels\n",
    "                batch_token_ids, batch_segment_ids, batch_labels = [], [], []\n",
    "\n",
    "# 加载预训练模型\n",
    "bert = build_transformer_model(\n",
    "    config_path=config_path,\n",
    "    checkpoint_path=checkpoint_path,\n",
    "    model=model_type,\n",
    "    return_keras_model=False,\n",
    ")\n",
    "\n",
    "# 搭建微调网络\n",
    "output = Lambda(lambda x: x[:, 0], name='CLS-token')(bert.model.output)\n",
    "output = Dense(\n",
    "    units=num_classes,\n",
    "    activation='softmax',\n",
    "    kernel_initializer=bert.initializer\n",
    ")(output)\n",
    "\n",
    "model = keras.models.Model(bert.model.input, output)\n",
    "model.summary()\n",
    "\n",
    "# 派生为带分段线性学习率的优化器。\n",
    "# 其中name参数可选，但最好填入，以区分不同的派生优化器。\n",
    "AdamLR = extend_with_piecewise_linear_lr(Adam, name='AdamLR')\n",
    "\n",
    "model.compile(\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    # optimizer=Adam(1e-5),  # 用足够小的学习率\n",
    "    optimizer=AdamLR(learning_rate=1e-4, lr_schedule={\n",
    "        1000: 1,\n",
    "        2000: 0.1\n",
    "    }),\n",
    "    metrics=['accuracy'],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/cql/anaconda3/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /home/cql/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Epoch 1/6\n",
      "1283/1283 [==============================] - 505s 394ms/step - loss: 1.2117 - accuracy: 0.6453\n",
      "val_acc: 0.75819, best_val_acc: 0.75819\n",
      "\n",
      "Epoch 2/6\n",
      "1283/1283 [==============================] - 501s 390ms/step - loss: 0.7487 - accuracy: 0.7713\n",
      "val_acc: 0.77636, best_val_acc: 0.77636\n",
      "\n",
      "Epoch 3/6\n",
      "1283/1283 [==============================] - 501s 390ms/step - loss: 0.6728 - accuracy: 0.7952\n",
      "val_acc: 0.77331, best_val_acc: 0.77636\n",
      "\n",
      "Epoch 4/6\n",
      "1283/1283 [==============================] - 503s 392ms/step - loss: 0.6439 - accuracy: 0.8030\n",
      "val_acc: 0.77354, best_val_acc: 0.77636\n",
      "\n",
      "Epoch 5/6\n",
      "1283/1283 [==============================] - 501s 391ms/step - loss: 0.6171 - accuracy: 0.8114\n",
      "val_acc: 0.77485, best_val_acc: 0.77636\n",
      "\n",
      "Epoch 6/6\n",
      "1283/1283 [==============================] - 502s 391ms/step - loss: 0.5898 - accuracy: 0.8197\n",
      "val_acc: 0.77126, best_val_acc: 0.77636\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 转换数据集\n",
    "train_generator = data_generator(train_data, batch_size)\n",
    "valid_generator = data_generator(valid_data, batch_size)\n",
    "\n",
    "# 定义评估方法\n",
    "def evaluate(data):\n",
    "    total, right = 0., 0.\n",
    "    for x_true, y_true in data:\n",
    "        y_pred = model.predict(x_true).argmax(axis=1)\n",
    "        y_true = y_true[:, 0]\n",
    "        total += len(y_true)\n",
    "        right += (y_true == y_pred).sum()\n",
    "    return right / total\n",
    "\n",
    "class Evaluator(keras.callbacks.Callback):\n",
    "    def __init__(self):\n",
    "        self.best_val_acc = 0.\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        val_acc = evaluate(valid_generator)\n",
    "        if val_acc > self.best_val_acc:\n",
    "            self.best_val_acc = val_acc\n",
    "            model.save_weights(best_model)\n",
    "        print(\n",
    "            u'val_acc: %.5f, best_val_acc: %.5f\\n' %\n",
    "            (val_acc, self.best_val_acc)\n",
    "        )\n",
    "\n",
    "evaluator = Evaluator()\n",
    "history = model.fit_generator(\n",
    "    train_generator.forfit(),\n",
    "    steps_per_epoch=len(train_generator),\n",
    "    epochs=6,\n",
    "    callbacks=[evaluator]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/cql/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "final val acc: 0.776364, cost 41 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.load_weights(best_model)\n",
    "bts = time.time()\n",
    "val_acc = evaluate(valid_generator)\n",
    "print(u'final val acc: %05f, cost %d s\\n' % (val_acc, time.time() - bts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': [0.6845892,\n",
       "  0.7733794,\n",
       "  0.78769547,\n",
       "  0.7971989,\n",
       "  0.80881625,\n",
       "  0.81889844],\n",
       " 'loss': [1.060315811418595,\n",
       "  0.7383742557945324,\n",
       "  0.6942238231849663,\n",
       "  0.6579965379631578,\n",
       "  0.6219681180992388,\n",
       "  0.5891616055682504]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = load_data('data/test.csv')\n",
    "test_generator = data_generator(test_data, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final val acc: 0.765590, cost 68 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bts = time.time()\n",
    "test_acc = evaluate(test_generator)\n",
    "print(u'final test acc: %05f, cost %d s\\n' % (test_acc, time.time() - bts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_test = []\n",
    "true_test = []\n",
    "for x_true, y_true in test_generator:\n",
    "    y_pred = model.predict(x_true).argmax(axis=1)\n",
    "    preds_test.extend(y_pred)\n",
    "    true_test.extend(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7655898266261206\n",
      "0.6481238765788617\n",
      "0.6627045745899003\n",
      "0.6498567518061146\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.accuracy_score(true_test, preds_test))\n",
    "print(metrics.precision_score(true_test, preds_test, average='macro'))\n",
    "print(metrics.recall_score(true_test, preds_test, average='macro'))\n",
    "print(metrics.f1_score(true_test, preds_test, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "import joblib\n",
    "le = joblib.load('model/class_le.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.68      0.63       493\n",
      "           1       0.73      0.53      0.62       654\n",
      "           2       0.48      0.78      0.59       253\n",
      "           3       0.66      0.72      0.69      1641\n",
      "           4       0.63      0.47      0.54       305\n",
      "           5       0.51      0.40      0.45       723\n",
      "           6       0.53      0.66      0.59      1125\n",
      "           7       0.62      0.70      0.66       599\n",
      "           8       0.72      0.84      0.78      1089\n",
      "           9       0.40      0.34      0.37       433\n",
      "          10       0.93      0.93      0.93      1155\n",
      "          11       0.63      0.62      0.63      3776\n",
      "          12       0.95      0.80      0.87       392\n",
      "          13       0.96      0.96      0.96     16925\n",
      "          14       0.74      0.66      0.70      6205\n",
      "          15       0.69      0.70      0.70      4942\n",
      "          16       0.37      0.36      0.36       147\n",
      "          17       0.41      0.64      0.50       338\n",
      "          18       0.88      0.96      0.92       749\n",
      "          19       0.74      0.83      0.78      1082\n",
      "          20       0.60      0.47      0.53       889\n",
      "          21       0.63      0.76      0.69       787\n",
      "          22       0.51      0.48      0.49      1843\n",
      "          23       0.78      0.77      0.78      1775\n",
      "          24       0.56      0.51      0.54      1804\n",
      "          25       0.62      0.64      0.63       518\n",
      "\n",
      "    accuracy                           0.77     50642\n",
      "   macro avg       0.65      0.66      0.65     50642\n",
      "weighted avg       0.77      0.77      0.76     50642\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(true_test, preds_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in le.classes_:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn .metrics import confusion_matrix  \n",
    "conmat = confusion_matrix(true_test, preds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd90eae8e48>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd90eaf2278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (20, 20))\n",
    "sns.heatmap(conmat, cmap='YlGnBu', annot=True, fmt=\"d\", ax = ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
